Conventionally, in order to improve running safety of a railway vehicle, online real-time monitoring for detecting an abnormality of a commercial vehicle (a railway vehicle in commercial operation) while the commercial vehicle is running has been implemented using various sensors which are attached to the commercial vehicle by monitoring a condition of the commercial vehicle running on a commercial line using the sensors (for example, see Patent Literatures 1 and 2).
However, when using the above method to detect abnormalities in railway vehicles while the railway vehicles are running, it is necessary to attach the sensors to all the railway vehicles, and time and labor are required to maintain and inspect the sensors and the like. Consequently, problems arise in that abnormalities in the railway vehicles cannot be easily detected and also that the method involves a great deal of expense.
To solve the aforementioned problems, a method has been proposed in which a wheel load sensor for measuring a wheel load of wheels is provided on a railroad track, and an abnormality of a railway vehicle is detected based on the size of an index represented by a wheel load measured by the wheel load sensor (for example, see Patent Literature 3).
According to the above method, abnormalities in railway vehicles can be detected more easily and with less cost in comparison to a case where a sensor is attached to each railway vehicle.
However, the respective methods described in Patent Literatures 1 to 3 are methods that compare an evaluation index with a predetermined threshold value and detects an abnormality of a railway vehicle depending on whether the evaluation index is larger or smaller than the threshold value. An appropriate threshold value for detecting an abnormality can vary depending on a structure of the railway vehicle, loading conditions, running conditions and the like. Consequently, in order to accurately detect an abnormality, it is necessary to determine a large number of threshold values for each of the conditions, which requires a large amount of time and labor.